Projections of ICU need by Country
Modeling current and future ICU demand.
by artdgn
Warning: This dashboard contains the results of a predictive model that was not built by an epidemiologist.
Note: Click a country name to open a search results page for that country’s COVID-19 news.
Based on data up to: 2020-12-23
World map (interactive)
Includes only countries with at least 1000 reported cases or at least 20 reported deaths.
- Details of estimation and prediction calculations are in Appendix and in Tables, as well as Plots of model predictions.
- New cases and new deaths refer to cases or deaths in the last 5 days.
Tip: Select columns to show on map to from the dropdown menus. The map is zoomable and draggable.
Tables
Projected need for ICU beds
Countries sorted by current estimated need, split into Growing and Recovering countries by current transmission rate. Only for countries with ICU need higher than 0.1 beds per 100k. More details in Appendix.
| Estimated current ICU need per 100k population | Estimated daily transmission rate | Projected ICU need per 100k In 14 days | Projected ICU need per 100k In 30 days | Pre-COVID ICU capacity per 100k | |
|---|---|---|---|---|---|
| 🇸🇪 Sweden | 8.51 | 5.1% ± 0.3% | 8.1 ± 0.3 | 7.6 ± 0.6 | 5.8 |
| 🇬🇧 United Kingdom | 7.92 | 7.8% ± 0.8% | 10.1 ± 1.0 | 13.1 ± 2.5 | 6.6 |
| 🇺🇸 US | 7.16 | 5.3% ± 0.3% | 6.8 ± 0.3 | 6.4 ± 0.5 | 34.7 |
| 🇱🇹 Lithuania | 7.14 | 5.8% ± 1.5% | 7.6 ± 1.5 | 8.2 ± 3.3 | 15.5 |
| 🇨🇿 Czechia | 6.00 | 6.6% ± 2.4% | 6.8 ± 2.0 | noisy data | 11.6 |
| 🇵🇦 Panama | 5.89 | 7.1% ± 1.5% | 6.5 ± 1.1 | 7.2 ± 2.4 | - |
| 🇳🇱 Netherlands | 5.28 | 6.6% ± 0.8% | 6.2 ± 0.6 | 7.3 ± 1.6 | 6.4 |
| 🇲🇽 Mexico | 4.34 | 5.7% ± 1.8% | 4.1 ± 0.8 | 3.8 ± 1.6 | 1.2 |
| 🇱🇻 Latvia | 3.84 | 6.2% ± 1.9% | 4.4 ± 1.1 | noisy data | 9.7 |
| 🇨🇴 Colombia | 3.47 | 6.8% ± 0.7% | 3.8 ± 0.3 | 4.2 ± 0.8 | - |
| 🇩🇪 Germany | 3.36 | 5.2% ± 2.3% | 3.4 ± 1.1 | noisy data | 29.2 |
| 🇧🇷 Brazil | 3.26 | 5.2% ± 1.6% | 3.0 ± 0.6 | 2.7 ± 1.1 | - |
| 🇩🇰 Denmark | 3.26 | 5.8% ± 0.6% | 3.5 ± 0.3 | 3.9 ± 0.7 | 6.7 |
| 🇨🇦 Canada | 3.12 | 5.2% ± 0.4% | 3.1 ± 0.2 | 3.0 ± 0.4 | 13.5 |
| 🇸🇰 Slovakia | 3.09 | 5.7% ± 1.9% | 3.3 ± 0.8 | noisy data | 9.2 |
| 🇪🇪 Estonia | 2.60 | 5.9% ± 1.4% | 2.9 ± 0.5 | 3.2 ± 1.3 | 14.6 |
| 🇦🇷 Argentina | 2.53 | 5.2% ± 1.3% | 2.4 ± 0.4 | 2.2 ± 0.7 | - |
| 🇹🇳 Tunisia | 2.46 | 6.0% ± 0.9% | 2.6 ± 0.3 | 2.8 ± 0.7 | - |
| 🇷🇺 Russia | 2.16 | 5.1% ± 0.2% | 2.1 ± 0.0 | 2.1 ± 0.1 | 8.3 |
| 🇿🇦 South Africa | 2.10 | 8.3% ± 1.4% | 2.8 ± 0.5 | 3.9 ± 1.3 | - |
| 🇮🇪 Ireland | 1.65 | 8.6% ± 1.6% | 2.4 ± 0.6 | 3.9 ± 1.9 | 6.5 |
| 🇨🇱 Chile | 1.56 | 5.9% ± 1.4% | 1.6 ± 0.3 | 1.7 ± 0.6 | - |
| 🇨🇾 Cyprus | 1.55 | 6.5% ± 3.3% | 1.9 ± 0.9 | noisy data | - |
| 🇮🇱 Israel | 1.26 | 7.3% ± 2.0% | 1.6 ± 0.4 | noisy data | - |
| 🇱🇧 Lebanon | 1.25 | 5.2% ± 1.0% | 1.2 ± 0.2 | 1.2 ± 0.4 | - |
| 🇵🇪 Peru | 1.23 | 5.5% ± 1.4% | 1.1 ± 0.2 | 1.0 ± 0.4 | - |
| 🇧🇾 Belarus | 1.12 | 5.1% ± 0.1% | 1.1 ± 0.0 | 1.1 ± 0.0 | - |
| 🇧🇴 Bolivia | 1.07 | 10.1% ± 4.5% | noisy data | noisy data | - |
| Eswatini | 0.80 | 10.5% ± 1.3% | 1.7 ± 0.3 | 3.6 ± 1.5 | - |
| 🇺🇾 Uruguay | 0.78 | 8.0% ± 1.3% | 1.1 ± 0.2 | 1.8 ± 0.7 | - |
| 🇩🇴 Dominican Republic | 0.69 | 5.6% ± 1.8% | 0.7 ± 0.2 | noisy data | - |
| 🇳🇦 Namibia | 0.62 | 8.4% ± 2.3% | 0.9 ± 0.3 | noisy data | - |
| 🇲🇷 Mauritania | 0.55 | 5.4% ± 1.0% | 0.6 ± 0.1 | 0.6 ± 0.2 | - |
| 🇸🇷 Suriname | 0.50 | 14.0% ± 4.8% | noisy data | noisy data | - |
| 🇮🇩 Indonesia | 0.45 | 5.5% ± 0.4% | 0.5 ± 0.0 | 0.5 ± 0.1 | 2.7 |
| 🇯🇲 Jamaica | 0.36 | 5.0% ± 0.9% | 0.4 ± 0.0 | 0.4 ± 0.1 | - |
| 🇸🇾 Syria | 0.27 | 5.3% ± 0.4% | 0.3 ± 0.0 | 0.3 ± 0.0 | - |
| 🇧🇭 Bahrain | 0.25 | 5.3% ± 0.5% | 0.2 ± 0.0 | 0.2 ± 0.0 | - |
| 🇮🇸 Iceland | 0.24 | 5.1% ± 1.1% | 0.2 ± 0.0 | 0.2 ± 0.1 | 9.1 |
| 🇯🇵 Japan | 0.19 | 5.2% ± 1.0% | 0.2 ± 0.0 | 0.2 ± 0.1 | 7.3 |
| 🇪🇬 Egypt | 0.18 | 6.9% ± 0.9% | 0.2 ± 0.0 | 0.3 ± 0.1 | - |
| 🇱🇸 Lesotho | 0.17 | 6.4% ± 0.1% | 0.2 ± 0.0 | 0.3 ± 0.0 | - |
| 🇰🇷 South Korea | 0.15 | 6.2% ± 0.6% | 0.2 ± 0.0 | 0.2 ± 0.0 | 10.6 |
| 🇲🇾 Malaysia | 0.14 | 5.3% ± 1.2% | 0.1 ± 0.0 | 0.2 ± 0.1 | 3.4 |
| 🇿🇼 Zimbabwe | 0.13 | 5.3% ± 1.2% | 0.1 ± 0.0 | 0.1 ± 0.1 | - |
| Estimated current ICU need per 100k population | Estimated daily transmission rate | Projected ICU need per 100k In 14 days | Projected ICU need per 100k In 30 days | Pre-COVID ICU capacity per 100k | |
|---|---|---|---|---|---|
| 🇸🇮 Slovenia | 10.41 | 4.7% ± 2.1% | 9.3 ± 2.5 | noisy data | 6.4 |
| 🇭🇷 Croatia | 8.08 | 3.4% ± 1.3% | 6.3 ± 1.1 | 4.6 ± 1.7 | - |
| 🇭🇺 Hungary | 7.34 | 3.2% ± 1.3% | 5.6 ± 0.9 | 3.9 ± 1.4 | 13.8 |
| 🇧🇬 Bulgaria | 7.14 | 2.7% ± 1.3% | 5.1 ± 0.9 | 3.4 ± 1.2 | - |
| 🇲🇰 North Macedonia | 7.12 | 3.7% ± 1.8% | 5.6 ± 1.2 | 4.1 ± 1.9 | - |
| 🇮🇹 Italy | 7.07 | 3.7% ± 0.5% | 5.7 ± 0.4 | 4.4 ± 0.6 | 12.5 |
| 🇲🇪 Montenegro | 6.33 | 4.1% ± 1.2% | 5.2 ± 0.8 | 4.1 ± 1.3 | - |
| 🇧🇦 Bosnia | 5.99 | 3.2% ± 1.0% | 4.5 ± 0.6 | 3.2 ± 0.8 | - |
| 🇬🇪 Georgia | 5.58 | 3.3% ± 1.3% | 4.4 ± 0.7 | 3.2 ± 1.2 | - |
| 🇨🇭 Switzerland | 5.47 | 4.5% ± 0.8% | 4.9 ± 0.5 | 4.2 ± 0.9 | 11.0 |
| 🇧🇿 Belize | 5.36 | 3.7% ± 0.4% | 4.1 ± 0.2 | 3.0 ± 0.3 | - |
| 🇷🇴 Romania | 4.79 | 3.7% ± 1.0% | 3.9 ± 0.5 | 3.0 ± 0.8 | - |
| 🇱🇺 Luxembourg | 4.74 | 3.3% ± 0.6% | 3.6 ± 0.3 | 2.6 ± 0.4 | 24.8 |
| 🇧🇪 Belgium | 4.74 | 4.1% ± 1.4% | 3.9 ± 0.6 | 3.1 ± 1.1 | 15.9 |
| 🇵🇱 Poland | 4.31 | 3.7% ± 1.2% | 3.5 ± 0.5 | 2.7 ± 0.9 | 6.9 |
| 🇵🇹 Portugal | 4.22 | 4.0% ± 1.1% | 3.6 ± 0.5 | 3.0 ± 1.0 | 4.2 |
| 🇲🇩 Moldova | 4.13 | 4.0% ± 1.1% | 3.4 ± 0.5 | 2.7 ± 0.8 | - |
| 🇷🇸 Serbia | 4.02 | 3.5% ± 0.2% | 3.2 ± 0.1 | 2.5 ± 0.2 | - |
| 🇦🇹 Austria | 3.59 | 2.9% ± 0.5% | 2.7 ± 0.2 | 1.9 ± 0.2 | 21.8 |
| 🇪🇸 Spain | 3.56 | 4.7% ± 1.1% | 3.2 ± 0.4 | 2.9 ± 0.9 | 9.7 |
| 🇦🇲 Armenia | 3.53 | 3.5% ± 1.3% | 2.7 ± 0.4 | 2.0 ± 0.7 | - |
| 🇫🇷 France | 3.48 | 4.3% ± 1.3% | 3.0 ± 0.5 | 2.5 ± 0.9 | 11.6 |
| 🇹🇷 Turkey | 3.44 | 2.0% ± 0.1% | 2.3 ± 0.0 | 1.4 ± 0.1 | 47.1 |
| 🇮🇷 Iran | 3.23 | 3.6% ± 0.1% | 2.5 ± 0.0 | 1.8 ± 0.0 | 4.6 |
| 🇦🇱 Albania | 3.09 | 3.4% ± 0.4% | 2.4 ± 0.1 | 1.8 ± 0.2 | - |
| 🇺🇦 Ukraine | 3.02 | 4.0% ± 0.8% | 2.6 ± 0.3 | 2.2 ± 0.5 | - |
| 🇦🇿 Azerbaijan | 2.59 | 3.7% ± 1.1% | 2.1 ± 0.3 | 1.6 ± 0.5 | - |
| 🇬🇷 Greece | 2.59 | 2.8% ± 0.6% | 1.9 ± 0.2 | 1.3 ± 0.2 | 6.0 |
| 🇯🇴 Jordan | 2.43 | 3.7% ± 0.7% | 1.9 ± 0.2 | 1.4 ± 0.3 | - |
| 🇲🇹 Malta | 2.38 | 4.2% ± 0.9% | 2.1 ± 0.3 | 1.8 ± 0.5 | - |
| 🇵🇸 West Bank and Gaza | 2.15 | 4.8% ± 0.6% | 2.0 ± 0.1 | 1.7 ± 0.3 | - |
| 🇵🇾 Paraguay | 1.60 | 4.9% ± 0.6% | 1.5 ± 0.1 | 1.4 ± 0.2 | - |
| 🇨🇷 Costa Rica | 1.58 | 4.5% ± 0.6% | 1.4 ± 0.1 | 1.2 ± 0.2 | - |
| 🇪🇨 Ecuador | 1.54 | noisy data | 1.2 ± 0.4 | noisy data | - |
| 🇲🇦 Morocco | 0.88 | 3.4% ± 1.1% | 0.7 ± 0.1 | 0.5 ± 0.2 | - |
| 🇱🇾 Libya | 0.81 | 4.2% ± 0.9% | 0.7 ± 0.1 | 0.6 ± 0.1 | - |
| 🇬🇾 Guyana | 0.79 | 4.7% ± 1.3% | 0.7 ± 0.1 | 0.7 ± 0.3 | - |
| 🇭🇳 Honduras | 0.68 | 4.4% ± 1.2% | 0.6 ± 0.1 | 0.5 ± 0.2 | - |
| 🇫🇮 Finland | 0.68 | 3.8% ± 0.5% | 0.6 ± 0.0 | 0.5 ± 0.1 | 6.1 |
| 🇬🇹 Guatemala | 0.63 | noisy data | 0.6 ± 0.2 | noisy data | - |
| 🇸🇻 El Salvador | 0.58 | noisy data | 0.5 ± 0.2 | noisy data | - |
| 🇮🇶 Iraq | 0.56 | 3.6% ± 0.3% | 0.4 ± 0.0 | 0.3 ± 0.0 | - |
| 🇧🇸 Bahamas | 0.54 | noisy data | 0.4 ± 0.1 | noisy data | - |
| 🇳🇴 Norway | 0.51 | 5.0% ± 1.0% | 0.5 ± 0.1 | 0.5 ± 0.2 | 8.0 |
| 🇰🇬 Kyrgyzstan | 0.49 | 3.0% ± 1.5% | 0.4 ± 0.1 | 0.3 ± 0.1 | - |
| 🇨🇻 Cabo Verde | 0.47 | 3.1% ± 1.2% | 0.4 ± 0.1 | 0.3 ± 0.1 | - |
| 🇰🇿 Kazakhstan | 0.39 | 4.4% ± 0.6% | 0.4 ± 0.0 | 0.3 ± 0.1 | 21.3 |
| 🇲🇲 Burma | 0.31 | 3.6% ± 0.2% | 0.3 ± 0.0 | 0.2 ± 0.0 | - |
| 🇴🇲 Oman | 0.30 | 4.8% ± 1.4% | 0.3 ± 0.0 | 0.2 ± 0.1 | 14.6 |
| 🇰🇼 Kuwait | 0.28 | 3.9% ± 0.5% | 0.2 ± 0.0 | 0.2 ± 0.0 | - |
| 🇦🇪 UAE | 0.26 | 4.6% ± 0.3% | 0.2 ± 0.0 | 0.2 ± 0.0 | - |
| 🇩🇿 Algeria | 0.24 | 3.3% ± 0.2% | 0.2 ± 0.0 | 0.1 ± 0.0 | - |
| 🇮🇳 India | 0.20 | 3.6% ± 0.4% | 0.2 ± 0.0 | 0.1 ± 0.0 | 5.2 |
| 🇸🇩 Sudan | 0.20 | noisy data | 0.2 ± 0.1 | noisy data | - |
| 🇹🇹 Trinidad and Tobago | 0.18 | noisy data | 0.2 ± 0.1 | noisy data | - |
| 🇳🇵 Nepal | 0.17 | 2.9% ± 0.2% | 0.1 ± 0.0 | 0.1 ± 0.0 | 2.8 |
| 🇵🇭 Philippines | 0.17 | 4.6% ± 0.7% | 0.2 ± 0.0 | 0.1 ± 0.0 | 2.2 |
| 🇵🇰 Pakistan | 0.16 | 3.7% ± 0.6% | 0.1 ± 0.0 | 0.1 ± 0.0 | 1.5 |
| 🇦🇫 Afghanistan | 0.12 | 4.2% ± 1.6% | 0.1 ± 0.0 | 0.1 ± 0.0 | - |
| 🇱🇰 Sri Lanka | 0.12 | 4.1% ± 0.8% | 0.1 ± 0.0 | 0.1 ± 0.0 | 2.3 |
| 🇲🇻 Maldives | 0.11 | 3.3% ± 1.4% | 0.1 ± 0.0 | 0.1 ± 0.0 | - |
| 🇰🇪 Kenya | 0.11 | 2.4% ± 1.1% | 0.1 ± 0.0 | 0.1 ± 0.0 | - |
Projected Affected Population percentages
Top 20 countries with most estimated recent cases. Sorted by number of estimated recent cases during the last 5 days. More details in Appendix.
| Estimated recent cases during last 5 days | Estimated total affected population percentage | Estimated daily transmission rate | Projected total affected percentage In 14 days | Projected total affected percentage In 30 days | Lagged fatality rate | |
|---|---|---|---|---|---|---|
| 🇺🇸 US | 1,567,708 | 11.0% | 5.3% ± 0.3% | 12.5% ± 0.1% | 14.3% ± 0.3% | 1.9% |
| 🇲🇽 Mexico | 649,748 | 18.8% | 5.7% ± 1.8% | 20.5% ± 0.7% | 22.5% ± 1.9% | 9.4% |
| 🇧🇷 Brazil | 646,143 | 14.8% | 5.2% ± 1.6% | 15.8% ± 0.4% | 16.9% ± 1.1% | 2.7% |
| 🇬🇧 United Kingdom | 469,272 | 10.4% | 7.8% ± 0.8% | 12.9% ± 0.4% | 16.8% ± 1.3% | 3.6% |
| 🇿🇦 South Africa | 307,328 | 12.0% | 8.3% ± 1.4% | 14.0% ± 0.6% | 17.4% ± 2.0% | 2.9% |
| 🇮🇳 India | 301,001 | 2.4% | 3.6% ± 0.4% | 2.5% ± 0.0% | 2.5% ± 0.0% | 1.5% |
| 🇮🇷 Iran | 254,512 | 14.4% | 3.6% ± 0.1% | 15.3% ± 0.0% | 16.1% ± 0.1% | 4.8% |
| 🇷🇺 Russia | 231,879 | 4.1% | 5.1% ± 0.2% | 4.7% ± 0.0% | 5.3% ± 0.1% | 1.9% |
| 🇨🇴 Colombia | 230,045 | 14.4% | 6.8% ± 0.7% | 16.1% ± 0.3% | 18.2% ± 0.8% | 2.8% |
| 🇮🇩 Indonesia | 192,927 | 1.8% | 5.5% ± 0.4% | 2.0% ± 0.0% | 2.3% ± 0.1% | 3.2% |
| 🇩🇪 Germany | 143,947 | 2.9% | 5.2% ± 2.3% | 3.5% ± 0.4% | 4.2% ± 1.1% | 2.0% |
| 🇮🇹 Italy | 139,539 | 8.7% | 3.7% ± 0.5% | 9.4% ± 0.1% | 10.1% ± 0.3% | 3.7% |
| 🇹🇷 Turkey | 128,634 | 4.1% | 2.0% ± 0.1% | 4.5% ± 0.0% | 4.8% ± 0.1% | 1.0% |
| 🇦🇷 Argentina | 105,061 | 13.7% | 5.2% ± 1.3% | 14.4% ± 0.2% | 15.2% ± 0.6% | 2.8% |
| 🇫🇷 France | 94,602 | 8.2% | 4.3% ± 1.3% | 8.7% ± 0.2% | 9.2% ± 0.5% | 2.5% |
| 🇪🇸 Spain | 84,325 | 8.9% | 4.7% ± 1.1% | 9.4% ± 0.2% | 10.0% ± 0.4% | 2.8% |
| 🇨🇦 Canada | 75,170 | 4.0% | 5.2% ± 0.4% | 4.7% ± 0.1% | 5.5% ± 0.2% | 3.0% |
| 🇵🇱 Poland | 69,343 | 6.9% | 3.7% ± 1.2% | 7.5% ± 0.2% | 8.1% ± 0.6% | 2.3% |
| 🇺🇦 Ukraine | 65,678 | 4.4% | 4.0% ± 0.8% | 4.9% ± 0.1% | 5.4% ± 0.3% | 1.9% |
| 🇳🇱 Netherlands | 63,473 | 6.2% | 6.6% ± 0.8% | 7.6% ± 0.3% | 9.5% ± 0.8% | 1.7% |
Methodology
- I'm not an epidemiologist. This is an attempt to understand what's happening, and what the future looks like if current trends remain unchanged.
- Everything is approximated and depends heavily on underlying assumptions.
- Projection is done using a simple SIR model (see examples) combined with the approach in Total Outstanding Cases:
- Growth rate is calculated over the 5 past days by averaging the daily growth rates.
- Confidence bounds are calculated by from the weighted standard deviation of the growth rate over the last 5 days. Model predictions are calculated for growth rates within 1 STD of the weighted mean. The maximum and minimum values for each day are used as confidence bands.
- Transmission rate, and its STD are calculated from growth rate and its STD using active cases estimation mentioned above.
- For projections (into future) very noisy projections (with broad confidence bounds) are not shown in the tables.
- Where the rate estimated from Total Outstanding Cases is too high (on down-slopes) recovery probability if 1/20 is used (equivalent 20 days to recover).
- Total cases are estimated from the reported deaths for each country:
- Each country has a different testing policy and capacity and cases are under-reported in some countries. Using an estimated IFR (fatality rate) we can estimate the number of cases some time ago by using the total deaths until today.
- IFRs for each country is estimated using the age adjusted IFRs from May 1 New York paper and UN demographic data for 2020. These IFRs can be found in
df['age_adjusted_ifr']column. Some examples: US - 0.98%, UK - 1.1%, Qatar - 0.25%, Italy - 1.4%, Japan - 1.6%. - The average fatality lag is assumed to be 8 days on average for a case to go from being confirmed positive (after incubation + testing lag) to death. This is the same figure used by "Estimating The Infected Population From Deaths".
- Testing bias adjustment: the actual lagged fatality rate is than divided by the IFR to estimate the testing bias in a country. The estimated testing bias then multiplies the reported case numbers to estimate the true case numbers (=case numbers if testing coverage was as comprehensive as in the heavily tested countries).
- ICU need is calculated and age-adjusted as follows:
- UK ICU ratio was reported as 4.4% of active reported cases.
- Using UKs ICU ratio, UK's testing bias, and IFRs corrected for age demographics we can estimate each country's ICU ratio (the number of cases requiring ICU hospitalisation).
- Active cases for ICU estimation are taken from the SIR model.
- Pre COVID-19 ICU capacities are from Wikipedia (OECD countries mostly) and CCB capacities in Asia.
The current capacities are likely much higher as some countries already doubled or even quadrupled their ICU capacities.